We first define the uncertainty of each object as \(\phi _i\) . Then, to measure this, we use MC Dropout [1]}. In particular, MC dropout can approximate an uncertainty of outputs, obtained from \(M\)  times of repeated forward passes while enabling dropout, by calculating a variance of them. To alleviate the excessive computational cost of MC Dropout caused by repeated forward passes through every layer, we introduce a slightly modified version of the original MC dropout. This approach is simple – instead of repeating whole forward passes, we only repeat forward passes with dropout in last fully connected layers of Faster R-CNN model's classification head and regression head, that are \(\texttt {cls\_head}\)  and \(\texttt {reg\_head}\)  in equation REF . Therefore, we can measure each object's uncertainty with minimal additional computational costs. Also, the suggested uncertainty measure can be easily implemented to other object detection frameworks, since the only modification required is simply repeating last forward steps \(M\)  times with dropout. The formal algorithm of the lightweight MC Dropout is shown in Algorithm REF .
